Digital disease detection - harnessing the Web for public health surveillance

Detecting influenza epidemics using search engine query data

Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295

Using Internet Searches for Influenza Surveillance

Polgreen, P. M., Chen, Y., Pennock, D. M., & Nelson, F. D. (2008) · Clinical Infectious Diseases · doi:10.1086/593098 · Cited by 567

Infodemiology: tracking flu-related searches on the web for syndromic surveillance.

Eysenbach, G. (2006) · PubMed · Cited by 414

Surveillance Sans Frontières: Internet-Based Emerging Infectious Disease Intelligence and the HealthMap Project

Brownstein, J. S., Freifeld, C. C., Reis, B. Y., & Mandl, K. D. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050151 · Cited by 413

ProMED-mail: An Early Warning System for Emerging Diseases

Yu, V. L., & Madoff, L. C. (2004) · Clinical Infectious Diseases · doi:10.1086/422003 · Cited by 269

Twitter discussions about the predicaments of robots in geriatric nursing: forecast of nursing robotics in aged care

Eriksson, H., & Salzmann‐Erikson, M. (2017) · Contemporary Nurse · doi:10.1080/10376178.2017.1364972 · Cited by 11

Evidence From Web-Based Dietary Search Patterns to the Role of B12 Deficiency in Non-Specific Chronic Pain: A Large-Scale Observational Study

Giat, E., & Yom-Tov, E. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.8667 · Cited by 11

Enhancing Syndromic Surveillance With Online Respondent-Driven Detection

Stein, M. L., van Steenbergen, J., Buskens, V., van der Heijden, P. G. M., Koppeschaar, C. E., Bengtsson, L., Thorson, A. E., & Kretzschmar, M. (2015) · American Journal of Public Health · doi:10.2105/ajph.2015.302717 · Cited by 11

Where Am I? Location Archetype Keyword Extraction from Urban Mobility Patterns

Kostakos, V., Juntunen, T., Gonçalves, J., Hosio, S. J., & Ojala, T. (2013) · PLoS ONE · doi:10.1371/journal.pone.0063980 · Cited by 11

Online interest regarding violent attacks, gun control, and gun purchase: A causal analysis

Gunn, L. H., ter Horst, E., Markossian, T. W., & Molina, G. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207924 · Cited by 11

A Pilot Randomized Controlled Trial of a Digital Intervention Aimed at Improving Food Purchasing Behavior: The Front-of-Pack Food Labels Impact on Consumer Choice Study

Harrington, R., Scarborough, P., Hodgkins, C. E., Raats, M. M., Cowburn, G., Dean, M., Doherty, A., Foster, C., Juszczak, E., Mhurchú, C. N., et al. (2018) · JMIR Formative Research · doi:10.2196/formative.9910 · Cited by 16

Harnessing digital data and data science to achieve 90–90–90 goals to end the HIV epidemic

Strathdee, S. A., Nobles, A. L., & Ayers, J. W. (2019) · Current Opinion in HIV and AIDS · doi:10.1097/coh.0000000000000584 · Cited by 11

Characterizing Influenza surveillance systems performance: application of a Bayesian hierarchical statistical model to Hong Kong surveillance data

Zhang, Y., Arab, A., Cowling, B. J., & Stoto, ­. A. (2014) · BMC Public Health · doi:10.1186/1471-2458-14-850 · Cited by 10

Use of a Web Forum and an Online Questionnaire in the Detection and Investigation of an Outbreak

Chester, T. L. S., Taylor, M., Sandhu, J., Forsting, S., Ellis, A., Stirling, R., & Galanis, E. (2011) · Online Journal of Public Health Informatics · doi:10.5210/ojphi.v3i1.3506 · Cited by 10

Head Lice Surveillance on a Deregulated OTC-Sales Market: A Study Using Web Query Data

Lindh, J. G., Magnusson, M., Grünewald, M., & Hulth, A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048666 · Cited by 10

New media methods for syndromic surveillance and disease modelling.

Walker, J. G. (2013) · CABI Reviews · doi:10.1079/pavsnnr20138031 · Cited by 10

‘Coming of Age’ of artificial intelligence: evolution of survivorship care through information technology

Hashmi, S. K. (2015) · Bone Marrow Transplantation · doi:10.1038/bmt.2015.271 · Cited by 10

Inflammation Thread Runs across Medical Laboratory Specialities

Nydegger, U. E., Lung, T., Risch, L., Risch, M., Escobar, P. M., & Bodmer, T. (2016) · Mediators of Inflammation · doi:10.1155/2016/4121837 · Cited by 10

Big data and learning analytics: Singular or plural?

Wilson, A. N., Thompson, T. L., Watson, C., Drew, V., & Doyle, S. (2017) · First Monday · doi:10.5210/fm.v22i4.6872 · Cited by 10

Population-scale hand tremor analysis via anonymized mouse cursor signals

White, R. W., & Horvitz, E. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0171-4 · Cited by 10

Public Water Waste Reporting: Contextual Correlates and Conservation Outcomes

Zhang, Y., Teodoro, M. P., & Switzer, D. (2021) · Water Resources Research · doi:10.1029/2020wr027805 · Cited by 11

Estimating the Incidence of Conjunctivitis by Comparing the Frequency of Google Search Terms With Clinical Data: Retrospective Study

Betancor, P. K., Tizek, L., Zink, A., Reinhard, T., & Böhringer, D. (2021) · JMIR Public Health and Surveillance · doi:10.2196/22645 · Cited by 10

Epidemic dreams: dreaming about health during the COVID-19 pandemic

Šćepanović, S., Aiello, L. M., Barrett, D., & Quercia, D. (2022) · Royal Society Open Science · doi:10.1098/rsos.211080 · Cited by 10

Initial Standardized Framework for Reporting Social Media Analytics in Emergency Care Research

Roland, D., Spurr, J., & Cabrera, D. (2018) · Western Journal of Emergency Medicine · doi:10.5811/westjem.2018.3.36489 · Cited by 10

Regional and temporal awareness of alpha-gal allergy: An infodemiological analysis using Google Trends

Iglesia, E. G. A., Stone, C. A., Flaherty, M. G., & Commins, S. P. (2019) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2019.12.003 · Cited by 10

Temporal Trends and Interest in Coronary Artery Calcium Scoring Over Time: An Infodemiology Study

Dzaye, O. D. A., Berning, P., Adelhoefer, S. J., Duebgen, M., Blankstein, R., Mahesh, M., Nasir, K., Blumenthal, R. S., Mortensen, M. B., & Blaha, M. J. (2021) · Mayo Clinic Proceedings Innovations Quality & Outcomes · doi:10.1016/j.mayocpiqo.2021.02.010 · Cited by 9

Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak

Nia, Z. M., Seyyed-Kalantari, L., Goitom, M., Garcia, B. R. M., Ahmadi, A., Asgary, A., Orbinski, J., Wu, J., & Kong, J. D. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103076 · Cited by 11

Early Signs of Financial Market Moves Reflected by Google Searches

Preis, T., & Moat, H. S. (2015) · Springer eBooks · doi:10.1007/978-3-319-14011-7_5 · Cited by 9

Digital Public Health Surveillance Tools for Alcohol Use and HIV Risk Behaviors

Garett, R., & Young, S. D. (2021) · AIDS and Behavior · doi:10.1007/s10461-021-03221-z · Cited by 9

The Promises and Perils of Social Media for Pediatric Rheumatology

Hausmann, J. S., & Weitzman, E. R. (2021) · Rheumatic Disease Clinics of North America · doi:10.1016/j.rdc.2021.09.005 · Cited by 9

Tweets Related to Motivation and Physical Activity for Obesity-Related Behavior Change: Descriptive Analysis

Park, A. (2022) · Journal of Medical Internet Research · doi:10.2196/15055 · Cited by 9

Online Work Force Analyzes Social Media to Identify Consequences of an Unplanned School Closure – Using Technology to Prepare for the Next Pandemic

Rainey, J. J., Kenney, J., Wilburn, B., Putman, A., Zheteyeva, Y. A., & O'Sullivan, M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0163207 · Cited by 9

Representations of physician's role and their impact on compliance

Venezia, A., Mossi, P., Venuleo, C., Savarese, G., & Salvatore, S. (2019) · PSICOLOGIA DELLA SALUTE · doi:10.3280/pds2019-002005 · Cited by 9

The Impact of Mobility on Shopping Preferences during the COVID-19 Pandemic: The Evidence from the Slovak Republic

Harantová, V., Kalašová, A., Kubíková, S., Mazanec, J., & Jordová, R. (2022) · Mathematics · doi:10.3390/math10091394 · Cited by 9

Participatory disease detection through digital volunteerism

Susumpow, P., Pansuwan, P., Sajda, N., & Crawley, A. W. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2567948.2579273 · Cited by 9

A Geospatial Analysis on the Potential Value of News Comments in Infectious Disease Surveillance

Cui, K., Cao, Z., Zheng, X., Zeng, D. D., Zeng, K., & Zheng, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22039-5_9 · Cited by 9

Tracking the Global Express

Fuller, S. S. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181f56757 · Cited by 9

Modeling Influenza by Modulating Flu Awareness

Smith, M. C., & Broniatowski, D. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39931-7_25 · Cited by 9

Big Data, az információs társadalom új paradigmája

Szűts, Z., & Yoo, J. (2016) · Információs Társadalom · doi:10.22503/inftars.xvi.2016.1.1 · Cited by 9

The politics of participatory epidemiology: Technologies, social media and influenza surveillance in the US

Blouin-Genest, G., & Miller, A. D. (2017) · Health Policy and Technology · doi:10.1016/j.hlpt.2017.02.001 · Cited by 9

Public Health Intelligence and the Detection of Potential Pandemics

French, M. A., & Mykhalovskiy, E. (2013) · doi:10.1002/9781118553923.ch2 · Cited by 9

Impact of COVID-19 Pandemic on Social Determinants of Health Issues of Marginalized Black and Asian Communities: A Social Media Analysis Empowered by Natural Language Processing

Whitfield, C. G., Liu, Y., & Anwar, M. M. (2024) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-024-01996-0 · Cited by 8

Unleashing the potential of artificial intelligence in infectious diseases

Zhou, H.-Y., Li, Y., Li, J., Meng, J., & Wu, A. (2025) · National Science Review · doi:10.1093/nsr/nwaf004 · Cited by 8

Evaluation of different machine learning approaches and input text representations for multilingual classification of tweets for disease surveillance in the social web

Magumba, M. A., & Nabende, P. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00528-5 · Cited by 9

VaccineWatch: a monitoring system of vaccine messages from social media data

Numnark, S., Ingsriswang, S., & Wichadakul, D. (2014) · IEEE International Conference on Systems Biology · doi:10.1109/isb.2014.6990742 · Cited by 8

Scales of governance: The role of surveillance in facilitating new diplomacy during the 2009–2010 H1N1 pandemic

Bell, M., Warren, A., & Budd, L. (2012) · Health & Place · doi:10.1016/j.healthplace.2012.07.005 · Cited by 8

A novel use of social media to evaluate the occurrence of skin lesions affecting wild dusky grouper, Epinephelus marginatus (Lowe, 1834), in Libyan coastal waters

Rizgalla, J., Shinn, A. P., Ferguson, H. W., Paladini, G., Jayasuriya, N. S., & Bron, J. E. (2016) · Journal of Fish Diseases · doi:10.1111/jfd.12540 · Cited by 8

Evaluation of the EpiCore outbreak verification system

Lorthe, T. S., Pollack, M. P., Lassmann, B., Brownstein, J. S., Cohn, E. L., Divi, N., Guibert, D. H., Olsen, J. M., Smolinski, M. S., & Madoff, L. C. (2018) · Bulletin of the World Health Organization · doi:10.2471/blt.17.207225 · Cited by 8

Big Data and Population-Based Decision Support

Krall, M., Gundlapalli, A. V., & Samore, M. H. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-398476-0.00013-0 · Cited by 8

Integrating Heterogeneous Healthcare Datasets and Visual Analytics for Disease Bio-surveillance and Dynamics

Ramanathan, A., Pullum, L., Steed, C. A., Quinn, S. P., Chennubhotla, C., & Parker, T. L. (2013) · Cited by 8

Adopting Big Data Analysis in the Agricultural Sector: Financial and Societal Impacts

Kashef, R. F. (2019) · Studies in big data · doi:10.1007/978-981-15-0663-5_7 · Cited by 8

Reading Tea Leaves in the Tourism Industry: A Case Study in the Gulf Oil SpillPreprint

Choi, H., & Liu, P. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1893078 · Cited by 8

Use of Large Language Models to Classify Epidemiological Characteristics in Synthetic and Real-World Social Media Posts About Conjunctivitis Outbreaks: Infodemiology Study

Deiner, M. S., Deiner, R. Y., Fathy, C. A., Deiner, N. A., Hristidis, V., McLeod, S. D., Bukowski, T., Doan, T. A., Seitzman, G. D., Lietman, T. M., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/65226 · Cited by 8

Using Web-Based Search Data to Study the Public’s Reactions to Societal Events: The Case of the Sandy Hook Shooting

Menachemi, N., Rahurkar, S., & Rahurkar, M. A. (2017) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.6033 · Cited by 6

Compliance Rates, Advantages, and Drawbacks of a Smartphone-Based Method of Collecting Food History and Foodborne Illness Data

Seitzinger, P., Osgood, N. D., Martin, W., Tataryn, J. R., & Waldner, C. (2019) · Journal of Food Protection · doi:10.4315/0362-028x.jfp-18-547 · Cited by 7

The Role of African Languages in Decolonising South African Universities

Mkhize, T. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671088.003 · Cited by 9

The Health of People: How the social sciences can improve population health

Science, C. F. S. (2017) · SAGE Publications Ltd eBooks · doi:10.4135/9781473998889 · Cited by 6

POSSIBLE FACTORS FOR FOOD SAFETY INFRACTION AND FRAUD CONTINUITY IN RESTAURANTS IN SAUDI ARABIA

F. ALSAYEQH, A. (2015) · Assiut Veterinary Medical Journal/Maǧallaẗ Asyūṭ al-ṭibiyyaẗ al-baytariyyaẗ · doi:10.21608/avmj.2015.170229 · Cited by 7

The effect of COVID-19 pandemic on sleep-related problems in adults and elderly citizens: An infodemiology study using relative search volume data

Cha, E. J., & Jeon, H. J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271059 · Cited by 7

The challenge to know and control: Disease outbreak surveillance and alerts in China and India

Davies, S. E. (2012) · Global Public Health · doi:10.1080/17441692.2012.699538 · Cited by 7

Nowcasting Credit Demand in Turkey with Google Trends Data

Zeybek, Ö., & Uğurlu, E. (2015) · RePEc: Research Papers in Economics · Cited by 7

History of Substance Abuse Research in the United States

VanGeest, J. B., Johnson, T. P., & Alemagno, S. A. (2017) · Springer eBooks · doi:10.1007/978-3-319-55980-3_1 · Cited by 7

Online surveillance of media health event reporting in Nepal: digital disease detection from a One Health perspective

Schwind, J. S., Norman, S. A., Karmacharya, D. B., Wolking, D. J., Dixit, S. M., Rajbhandari, R. M., Mekaru, S. R., & Brownstein, J. S. (2017) · BMC International Health and Human Rights · doi:10.1186/s12914-017-0134-2 · Cited by 7

Algorithmic futures

Duclos, V. (2019) · Medicine Anthropology Theory · doi:10.17157/mat.6.3.660 · Cited by 10

Overview of the Health Search and Data Mining (HSDM 2020) Workshop

Eickhoff, C., Kim, Y., & White, R. W. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371879 · Cited by 7

Populist Conspiracy Theories and Candidate Preference in the U.S.

Sawyer, P. S., & Sawyer, P. (2020) · ACTA POLITOLOGICA · doi:10.14712/1803-8220/12_2020 · Cited by 7

Data Collection

Tijhuis, M. J., Finger, J. D., Slobbe, L., Sund, R., & Tolonen, H. K. (2018) · Springer eBooks · doi:10.1007/978-3-319-76562-4_4 · Cited by 7

Big data en sanidad en España: la oportunidad de una estrategia nacional

Parra-Calderón, C. L. (2015) · Gaceta Sanitaria · doi:10.1016/j.gaceta.2015.10.005 · Cited by 7

A review on health system-based surveillance for acute flaccid paralysis: technological advancements, challenges, and outlooks

Gemechu, H., Biru, G., Gebremeskel, E., Nemomssa, H. D., Dese, K., Wakjira, E., Demlew, G., Yohannes, D., Abdi, K. L., Murad, H., et al. (2025) · Journal of Health Population and Nutrition · doi:10.1186/s41043-025-01115-7 · Cited by 6

What methods are used to examine representation of mental ill-health on social media? A systematic review

Tudehope, L., Harris, N., Vorage, L., & Sofija, E. (2024) · BMC Psychology · doi:10.1186/s40359-024-01603-1 · Cited by 9

Privacy-Preserving-Enabled Lightweight COVID-19 Simulation Model for Mobile Intelligent Application

Zhang, S., Bi, G., Qi, J., Yang, Y., Kong, X., Nan, F., Zhou, M., & Yang, P. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3162687 · Cited by 6

Feasibility and Design Factors for Home-Based Pulmonary Rehabilitation of Patients With Chronic Obstructive Pulmonary Disease and Chronic Lung Diseases Based on a People-Object-Environment Framework: Qualitative Interview Study

Chien, S.-Y., Wong, M., Tseng, W., Hu, H., & Cho, H. (2024) · JMIR Human Factors · doi:10.2196/51150 · Cited by 6

Increased emergency department chief complaints of fever identified the influenza (H1N1) pandemic before outpatient symptom surveillance

Shimoni, Z., Rodrig, J., Dusseldorp, N., Niven, M. J., & Froom, P. (2011) · Environmental Health and Preventive Medicine · doi:10.1007/s12199-011-0213-2 · Cited by 6

Whiplash Syndrome Reloaded: Digital Echoes of Whiplash Syndrome in the European Internet Search Engine Context

Noll‐Hussong, M. (2017) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.7054 · Cited by 6

A multi-site study on walkability, data sharing and privacy perception using mobile sensing data gathered from the mk-sense platform

Hernandez-Cruz, N., Bert Arnrich, A., Favela, J., Ersoy, C., Demiray, B., & Fontecha, J. (2017) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-017-0642-x · Cited by 6

Renaming Korean epilepsy: Where are we?

Ji, K. (2018) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2018.08.003 · Cited by 6

Knowledge discovery in open data for epidemic disease prediction

Wu, C., & Kao, S. (2021) · Health Policy and Technology · doi:10.1016/j.hlpt.2021.01.001 · Cited by 6

Disease Tracking in GCC Region Using Arabic Language Tweets

Ilyas, M., & Alowibdi, J. S. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3186357 · Cited by 6

Sentinel Nodes Identification for Infectious Disease Surveillance on Temporal Social Networks

Geng, J., Li, Y., Zhang, Z., & Tao, L. (2019) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3350546.3360739 · Cited by 6

Digital public interest on coronavirus information and social distancing: a Brazilian nationwide analysis using an infodemiology approach

Martins‐Filho, P. R., Araújo, A. A. D. S., Quintans‐Júnior, L. J., & Santos, V. S. (2020) · Journal of Travel Medicine · doi:10.1093/jtm/taaa154 · Cited by 6

Global Surveillance Systems for Emerging Infectious Diseases: A Critical Review of Infrastructure, Policy, and Data Sharing

Ahmed, R. (2025) · Premier Journal of Data Science · doi:10.70389/pjds.100004 · Cited by 5

Reframing veterinary vaccine quality control: Integrating alternative assays within lifecycle-based regulatory frameworks

Abousenna, M. S. (2026) · Veterinary Immunology and Immunopathology · doi:10.1016/j.vetimm.2026.111109 · Cited by 6

Artificial intelligence and healthcare

Mittal, A., Afsar, A. P., Tayal, A., & Shetty, M. K. (2023) · MAMC Journal of Medical Sciences · doi:10.4103/mamcjms.mamcjms_27_23 · Cited by 5

Evaluation of Google Search Trends for Liver Diseases in Europe

Teufel, A., Itzel, T., Zimmermann, A., Dumitrascu, D. L., Bugianesi, E., Valenti, L. V. C., Castéra, L., Carrieri, P., Crespo, J., Romero‐Gómez, M., et al. (2024) · Journal of Gastrointestinal and Liver Diseases · doi:10.15403/jgld-5477 · Cited by 5

An Ensemble Neural Network Model for Benefiting Pregnancy Health Stats from Mining Social Media

Warikoo, N., Chang, Y., Dai, H.-J., & Hsu, W.-L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03520-4_1 · Cited by 5

Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research

Abad, Z. S. H., Butler, G., Thompson, W., & Lee, J. (2021) · JMIR Public Health and Surveillance · doi:10.2196/32355 · Cited by 8

The Ethics of National Security Intelligence Institutions

Henschke, A., Miller, S., Alexandra, A., Walsh, P. F., & Bradbury, R. (2024) · Routledge eBooks · doi:10.4324/9781003106449 · Cited by 5

Surveillance for Neisseria meningitidis Disease Activity and Transmission Using Information Technology

Ahmed, S., Oviedo‐Orta, E., Mekaru, S. R., Freifeld, C. C., Tougas, G. H., & Brownstein, J. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0127406 · Cited by 5

Social media trends for health librarians: a primer on using social media for clinical disease surveillance

Giustini, D. (2012) · Journal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada · doi:10.5596/c2012-025 · Cited by 5

The Role of Informal Digital Surveillance Systems Before, During and After Infectious Disease Outbreaks: A Critical Analysis

Magid, A., Gesser‐Edelsburg, A., & Green, M. S. (2018) · NATO science for peace and security series. A, Chemistry and biology · doi:10.1007/978-94-024-1263-5_14 · Cited by 5

Google arama hacmi verileri ile Türkiye’de hastalık farkındalık günlerinin etkinliğinin değerlendirilmesi

Donar, G. B. (2020) · Mersin üniversitesi sağlık bilimleri dergisi/Mersin üniversitesi sağlık bilimleri dergisi · doi:10.26559/mersinsbd.691939 · Cited by 4

Decolonising Our Minds, Decolonising Our Languages

Motinyane, M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671088.005 · Cited by 5

Technological Advances in Clinical Assessment

Heinz, M. V., Thomas, N., Nguyen, N. D., Griffin, T. Z., & Jacobson, N. C. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818697-8.00171-0 · Cited by 6

A quantifiable framework for ‘Covid-19 exposure’ to support the Vaccine prioritization and resource allocation for resource-constraint societies

Biswas, R., Arya, S., & Arya, K. (2021) · Urban Governance · doi:10.1016/j.ugj.2021.11.001 · Cited by 5

Google Trends for Data Mining. Study of Czech Towns

Horák, J., Ivan, I., Kukuliač, P., Inspektor, T., Devecka, B., & Návratová, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40495-5_11 · Cited by 5

Digital disease detection: Application of machine learning in community health informatics

Boonchieng, E., & Duangchaemkarn, K. (2016) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse.2016.7748841 · Cited by 5

Symptom-based data preprocessing for the detection of disease outbreak

Duangchaemkarn, K., Chaovatut, V., Wiwatanadate, P., & Boonchieng, E. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037393 · Cited by 5

Characterizing online health and wellness information consumption: A study

Nigam, A., Johnson, R. A., Wang, D., & Chawla, N. V. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.04.005 · Cited by 5

COVID-19, flattening the curve: recommendations towards control and managing a second wave

Fenemigho, I., Ukponmwan, E., Nnakwue, E. C., Udoete, I., Asuzu, C., Adaralegbe, A., & Effiong, U. (2020) · Journal of Global Health Reports · doi:10.29392/001c.14151 · Cited by 5

Tracing and responding to foodborne illness

Tegtmeyer, R., Potts, L., & Hart-Davidson, W. (2012) · ACM International Conference on Design of Communication (SIGDOC) · doi:10.1145/2379057.2379131 · Cited by 5

Descripción clínica y epidemiológica de los primeros casos de la gripe nueva A (H1N1) atendidos en España

Miró, Ò., Rovira, E., Blanco, A. J. A., Salmerón, J. M., Artigas, J. M. G., Cervera, C., García, A. T., Marcos, M. Á., Pumarola, T., & Sánchez, M. S. (2009) · Dialnet (Universidad de la Rioja) · Cited by 5

COVID-19 Outbreak, Social Response, and Early Economic Effects: A Global VAR Analysis of Cross-Country InterdependenciesPreprint

Milani, F. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3682018 · Cited by 5

Multi-Level Sociotechnical Systems Approach to Human-Centered AI

Schoenherr, J. R., & Michael, K. (2026) · Springer eBooks · doi:10.1007/978-981-97-8440-0_100-1 · Cited by 5

Discussion on “Real-time monitoring of events applied to syndromic surveillance”

Megahed, F. M. (2019) · Quality Engineering · doi:10.1080/08982112.2018.1530358 · Cited by 4

mk-sense: An Extensible Platform to Conduct Multi-institutional Mobile Sensing Campaigns

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